Clinical Terminology Shock and Awe

Fifth Annual Workshop of the Clinical and Translational Science Ontology Group

Announcement

Are clinical terminologies and other healthcare data standards realizing their goals of system interoperability and data compatibility? Do they enhance or detract from EHR usability? How usable are the terminologies and standards themselves? Can systems developers understand them enough to incorporate them into EHR design? Can clinicians understand them well enough to reliably communicate to both computers and humans? Can researchers benefit from these standards? Do they enable translational science? Do they support or inhibit research reproducibility? What work remains to be done? What approaches are needed to realize the vision of interoperability and data compatibility?

The Clinical and Translational Science Ontology Group invites you to join us this September in Buffalo as we assess the state of the art in clinical terminologies and ontologies and build a research agenda for closing the "interoperability" and "data compatibility" gap. Our keynote speaker will be Dr. Stefan Schulz who will address the reliability of professional SNOMED CT coding and what ontological approaches might help to improve it.

Venue: Ramada Hotel, Amherst, NY

Special "UB" room rate: $89 (2 queen beds), $99 (1 king bed)

Rationale

The CTSA Program has always emphasized the need for data standards to promote sharing and comparison of data across the CTSA Consortium and beyond. Yet creation and adoption of such standards is still painfully slow. Urgent action remains necessary. History shows the high value of standard terms, definitions, and symbols (i.e. ontology) to science. But the creation and adoption of such standards often takes decades. Translational science requires a consistent set of standard ontologies spanning all scales, from molecule to organism to population. But clinical terminologies at the macroscale – such as SNOMED and ICD – inhibit translational science. They are inconsistent with successful micro-scale ontologies such as the Gene Ontology, and they also cannot change rapidly with the advance of science. Furthermore, we will address additional issues with clinical terminologies as they currently exist, specifically the problem that even professional coding with them has poor inter-coder reliability. This situation degrades the quality of terminology-encoded data below acceptable research standards. Lastly, we believe confusing and incoherent terminologies are a barrier to end-user usability of resources like EHRs and the data they produce.

Translational science must settle on standards that evolve in a way that is closely tied to scientific advance. In the case of chemical symbols and SI Units adoption proceeded in three overlapping stages. First came widespread recognition and understanding of the problem. Second, influential stakeholders helped to develop, test, and select appropriate standards. Third, once scientifically useful standards emerged, the community enforced them via peer review. How can we accelerate progress on clinical ontologies through all three stages? How, in other words, can we create and implement standard clinical ontologies that are open and sufficiently well disseminated to achieve consortium-wide adoption?

A key barrier to adoption of ontologies is the widespread perception among IT companies and programmers, especially EHR developers, that ontology is impractical and inaccessible to them. And thus ontology is not relevant. The perception is that merely adopting standard value sets in their proprietary information models is sufficient. But it is not. How do we demonstrate the value, and a practical and accessible path forward, for the adoption of BFO / RO / OBI / IAO / OGMS / HPO / GO / HDO / ChEBI / DrOn / OMRSE and other OBO ontologies in EHRs, i2b2, REDCap, and other systems in support of translational science?

This workshop will convene stakeholders interested in identifying ways to harmonize clinical terminology resources with their counterparts at the molecular level and make substantial progress in their implementation in every day clinical and research information systems, especially the EHR. A consistent framework for ontologies that enable interoperability of systems, compatibility of data, and research reproducibility is the vision.

Goals

The Clinical and Translational Science Ontology Group was established in 2012 to leverage the use of common ontologies to support different aspects of information-driven clinical and translational research. The focus of this meeting is to explore new and existing uses of common ontologies to support creation, sharing, and analysis of clinical data.

Like its predecessors in the series, this meeting is designed to bring together clinical and translational scientists from across the CTSA Consortium who are interested in using ontologies to promote discoverability and interoperability of biomedical data.

​Specific themes will include:

• A​dvanc​ing​ reproducibility ​of clinical and translational research (BFO, OBI, LOINC)

• ​Advancing EHR interoperability​ (addressing SNOMED / CCD and meaningful use regulations whereby SNOMED CT is required for recording problem list and smoking status, and CCD is required for care summary)

• Improving EHR data usability​

• ​I​mproving SNOMED / CCD / c-CDA usability

• SNOMED (collaborating with LOINC, GMDN, ICNP, ICD-11, OrphaNet, FMA) as a Clinical Terminology Foundry

• Advancing interoperability of clinical data generally and of EHR data in particular

• ​Improving EHR usability in the clinic

• i2b2, PCORnet, OMOP and other approaches to clinical data sharing

Persons interested in attending or in presenting at the meeting should write to Barry Smith.

Keynote by Stefan Schulz

Coding clinical narratives: Causes and cures for inter-expert disagreements

Abstract: We will investigate the fitness for use of clinical terminologies to enable EHR interoperability. Information extraction from clinical narratives using NLP was identified as an important use case. For this purposes, terminology experts built a gold standard annotation for SNOMED CT and a UMLS extract, where shockingly low inter-annotator agreement values resulted. This talk will elucidate typical reasons for disagreement and point out how disgreement can be partially mitigated for SNOMED CT by exploiting its axiomatic basis, at least partially built on ontological grounds.

Biosketch: Stefan Schulz is a professor of Medical Informatics at the Medical University of Graz, Austria. Trained as physician, his research encompasses electronic health records, medical language processing, biomedical terminologies, and the application of formal ontologies for biomedical knowledge representation. He has contributed to the development of clinical terminology standards such as WHO classifications and SNOMED CT.

Schedule

Wednesday, September 7

8:00am Registration and Breakfast

6:00pm Dinner

Thursday, September 8

8:00am Registration and Breakfast

4:00pm Close

Sponsors

Department of Biomedical Informatics, University at Buffalo

National Center for Ontological Research, Buffalo

Organizing Committee

Barry Smith (University at Buffalo)

William Hogan (University of Florida)

Participants

Sivaram Arabandi (Health 2.0, Houston)

Olivier Bodenreider (National Library of Medicine)

Jonathan Bona (Buffalo)

Mathias Brochhausen (Arkansas)

Werner Ceusters (Buffalo)

Kei-Hoi Cheung (Yale / VA Connecticut Healthcare System)

Alexander Diehl (Buffalo)

Willian Duncan (Buffalo)

Peter Elkin (Buffalo)

Fernanda Farinelli (Minas Gerais, Brazil)

Yongqun "Oliver" He (Ann Arbor)

William Hogan (Gainesville)

Mark Jensen (United Nations Environment Programme)

Ross Koppel (University of Pennsylvania)

Asiyah Lin (Food and Drug Administration)

Sina Madani (MD Anderson Cancer Center)

Øystein Nytrø (Trondheim, Norway)

Edison Ong (Ann Arbor)

Jihad Obeid (Charleston)

Jose Parente de Oliveira (ITA, Brazil)

Rasmus Rosenberg Larsen (Buffalo)

Alan Ruttenberg (Buffalo)

Stefan Schulz (Graz, Austria)

Barry Smith (Buffalo)

Dagobert Soergel (Buffalo)

Ram Sriram (HealthIT, National Institute of Standards and Technology)